The Senior Machine Learning Engineer sits on the Data Science Team within our Strategic Analytics & Intelligence Organization (SAI) and exists to help the CMG (Commercial, Medical and Government) organization achieve its vision by unlocking value from data quicker and more effectively. As a center of excellence, the Commercial Data Science team leverages advanced data science capabilities, working across CMG to develop strategic and holistic solutions by identifying and leading innovative analytic projects & pilots to enable patient and customer solutions.

The Opportunity

The Senior Machine Learning Engineer will bring a robust understanding of machine learning operations (MLOps) to manage and contribute to our machine learning initiatives.

• The Senior Machine Learning Engineer will design, enhance, scale, and maintain machine learning solutions from conception to deployment in a production environment.

• You will collaborate with data scientists, software engineers, and business stakeholders to translate business requirements into scalable ML systems.

• You will also architect and uphold MLOps pipelines using job scheduling frameworks to streamline data preparation, training, deployment, and machine learning model lifecycles.

• In addition to ensuring best practices in code quality, version control, and CI/CD for machine learning pipelines, you will also design and implement robust monitoring systems for deployed models to track performance, data drift, and anomalies.

• The MLOps Engineer will also pioneer and administer model retraining, A/B testing, and progressive deployment strategies for continuous model enhancement.

• You will contribute to the company's machine learning architecture to support scalable and repeatable model training and deployment.

• Facilitate the creation of automated processes for model validation and testing.

• Position is located in South San Francisco and relocation benefits are not available.

• You hold a Bachelor's Degree in Computer Science or related technical discipline

• You have at least 7 years of experience working in a machine learning engineer role, with a minimum of 2 years in an MLOps-focused position.

• Your expertise in ML frameworks (e.g., TensorFlow, PyTorch), programming languages (e.g., Python, Scala, Java), MLOps technologies (e.g., Kubeflow, MLflow, AWS Sagemaker), and job scheduling frameworks (e.g., Apache Airflow,AWS Step Functions) is strong.

• You have a solid understanding and experience with cloud services and containerization technologies/platforms, particularly AWS and Kubernetes.

• Proficient with software engineering best practices, including agile development, code reviews, SCM, build processes, testing, and operations.

• Experience with distributed computing and big data technologies (e.g., Hadoop, Spark).

Preferred Skills

• You have a Masters Degree in Computer Science or related discipline

• Experience building and optimizing structured and unstructured big data pipelines, architectures, and datasets.

• Excellent communication skills to effectively collaborate with cross-functional teams.

The expected salary range for this position based on the primary location of CA is $188,200 - $349,400. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.

Benefits

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Genentech is an equal opportunity employer, and we embrace the increasingly diverse world around us. Genentech prohibits unlawful discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin or ancestry, age, disability, marital status and veteran status.